摘要
随着碳排放问题的日益紧迫,风能作为清洁可再生能源受到了广泛的关注与研究。由于风能的不确定性和间歇性,导致风能利用率不足,无法充分发挥其能源效益。因此,针对风的混沌特征提出了一种基于EMD的线性和非线性相结合的混合模型(LNH-EMD)预测方法,以实现对风速的超短期预测。首先将历史风速序列通过EMD分解成多组相对简单的模态分量;然后分别对各分量进行线性判别:对于非线性特征明显的模态分量通过构建其相空间,进行相空间重构预测;对于其他模态分量采用OLS拟合预测;最后将各分量的预测结果进行经验模态重构得到最终的风速预测结果。以典型非线性信号和NERL真实风速数据为实例进行预测实验,与Gray、WA和LSTM模型进行对比,结果表明:所提的LNH-EMD风速预测模型具有较高的预测精度与运行效率。
As the issue of carbon emissions becomes increasingly urgent,wind energy,as a clean and renewable energy source,has attracted widespread attention and research.However,due to the uncertainty and intermittence of wind,the utilization rate of wind energy is low and its energy efficiency cannot be fully exerted.Therefore,we propose a linear and nonlinear hybrid model based on empirical mode decomposition(LNH-EMD)prediction approach to forecast ultra-short-term wind speed according to the chaotic characteristics of wind.Firstly,we decompose the historical wind speed series into several groups of relatively simple modal components by EMD.Secondly,we performe a linear judgment of each component separately.We complete the prediction proceeding of nonlinear modal components based on the phase-space reconstruction,and use OLS fitting to forecast other modal components.Finally,we obtain the wind speed prediction results through empirical mode reconstruction of each component.The prediction results of typical nonlinear signals and real wind speed data from NERL illustrate that:the proposed LNH-EMD wind speed prediction model exhibits higher prediction accuracy and operation efficiency when compared to Gray,WA and LSTM prediction models.
作者
邢雪亮
贾利民
陈卓
XING Xueiang;JIA Limin;CHEN Zhuo(China Institute of Energy and Transportation Integrated Development(North China Electric Power University),Beijing 102206,China;State Key Laboratory of Advanced Rail Autonomous Operation(Beijing Jiaotong University),Beijing 100044,China)
出处
《华北电力大学学报（自然科学版）》
CAS
北大核心
2024年第6期41-48,I0004-I0006,共11页
Journal of North China Electric Power University：Natural Science Edition
基金
国家重点研发计划(2021YFB2601402).
关键词
超短期预测
风速预测
经验模态分解
混合模型
相空间重构
ultra-short-term prediction
wind speed prediction
EMD
hybrid model
phase space reconstruction
